{
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  "metadata": {
    "colab": {
      "name": "The USA Elections.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "yaR9gCSE7f1A",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Polls and Donors\n",
        "\n",
        "## Poll Data Set"
      ]
    },
    {
      "metadata": {
        "id": "8W7KoKNCJ0ZF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "In this Data Project, we will be looking at data from the 2012 USA election.\n",
        "\n",
        "In this project we will analyze two datasets. The first data set will be the results of political polls. We will analyze this aggregated poll data and answer some questions:\n",
        "\n",
        "  1. Who was being polled and what was their party affiliation?\n",
        "  2. Did the poll results favor Romney or Obama?\n",
        "  3. How do undecided voters effect the poll?\n",
        "  4. Can we account for the undecided voters?\n",
        "  5. How did voter sentiment change over time?\n",
        "  6. Can we see an effect in the polls from the debates?\n",
        "  \n",
        "The data for the polls will be obtained from HuffPost Pollster. You can check their website [here](http://elections.huffingtonpost.com/pollster)."
      ]
    },
    {
      "metadata": {
        "id": "XPjrz0HA7Do0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# data manipulation libraries\n",
        "import pandas as pd\n",
        "from pandas import Series, DataFrame\n",
        "import numpy as np\n",
        "\n",
        "# visualization libraries\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# for timestamps\n",
        "from datetime import datetime\n",
        "\n",
        "# for gathering data from the web\n",
        "import requests\n",
        "\n",
        "# to work with the csv file\n",
        "from io import StringIO\n",
        "\n",
        "sns.set_style('whitegrid')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kqkOfVICK2tE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Import Data"
      ]
    },
    {
      "metadata": {
        "id": "31AtCkPT9Hzr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# url link for the poll data in csv form\n",
        "url = \"http://elections.huffingtonpost.com/pollster/2012-general-election-romney-vs-obama.csv\"\n",
        "\n",
        "# use requests to get the information in text form (from CSV to text)\n",
        "source = requests.get(url).text\n",
        "\n",
        "# use StringIO to avoid an IO error with pandas\n",
        "poll_data = StringIO(source)\n",
        "\n",
        "# set poll data as pandas DataFrame\n",
        "poll_df = pd.read_csv(poll_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "34QO2K9-rdGp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Analyze Data"
      ]
    },
    {
      "metadata": {
        "id": "CfuzOMkcrcMN",
        "colab_type": "code",
        "outputId": "7f5f3f47-58de-4987-d080-0fd014a0ed09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "cell_type": "code",
      "source": [
        "# data information\n",
        "poll_df.info()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 586 entries, 0 to 585\n",
            "Data columns (total 17 columns):\n",
            "Pollster                  586 non-null object\n",
            "Start Date                586 non-null object\n",
            "End Date                  586 non-null object\n",
            "Entry Date/Time (ET)      586 non-null object\n",
            "Number of Observations    564 non-null float64\n",
            "Population                586 non-null object\n",
            "Mode                      586 non-null object\n",
            "Obama                     586 non-null float64\n",
            "Romney                    586 non-null float64\n",
            "Undecided                 423 non-null float64\n",
            "Other                     202 non-null float64\n",
            "Pollster URL              586 non-null object\n",
            "Source URL                584 non-null object\n",
            "Partisan                  586 non-null object\n",
            "Affiliation               586 non-null object\n",
            "Question Text             0 non-null float64\n",
            "Question Iteration        586 non-null int64\n",
            "dtypes: float64(6), int64(1), object(10)\n",
            "memory usage: 77.9+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Z3zyvP8o7Q3A",
        "colab_type": "code",
        "outputId": "3cd5cef5-64c6-40aa-96ca-e4b353d02fff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        }
      },
      "cell_type": "code",
      "source": [
        "# top 10 rows\n",
        "poll_df.head(10)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pollster</th>\n",
              "      <th>Start Date</th>\n",
              "      <th>End Date</th>\n",
              "      <th>Entry Date/Time (ET)</th>\n",
              "      <th>Number of Observations</th>\n",
              "      <th>Population</th>\n",
              "      <th>Mode</th>\n",
              "      <th>Obama</th>\n",
              "      <th>Romney</th>\n",
              "      <th>Undecided</th>\n",
              "      <th>Other</th>\n",
              "      <th>Pollster URL</th>\n",
              "      <th>Source URL</th>\n",
              "      <th>Partisan</th>\n",
              "      <th>Affiliation</th>\n",
              "      <th>Question Text</th>\n",
              "      <th>Question Iteration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Politico/GWU/Battleground</td>\n",
              "      <td>2012-11-04</td>\n",
              "      <td>2012-11-05</td>\n",
              "      <td>2012-11-06T08:40:26Z</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>Likely Voters</td>\n",
              "      <td>Live Phone</td>\n",
              "      <td>47.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
              "      <td>http://www.politico.com/news/stories/1112/8338...</td>\n",
              "      <td>Nonpartisan</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>YouGov/Economist</td>\n",
              "      <td>2012-11-03</td>\n",
              "      <td>2012-11-05</td>\n",
              "      <td>2012-11-26T15:31:23Z</td>\n",
              "      <td>740.0</td>\n",
              "      <td>Likely Voters</td>\n",
              "      <td>Internet</td>\n",
              "      <td>49.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
              "      <td>http://cdn.yougov.com/cumulus_uploads/document...</td>\n",
              "      <td>Nonpartisan</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gravis Marketing</td>\n",
              "      <td>2012-11-03</td>\n",
              "      <td>2012-11-05</td>\n",
              "      <td>2012-11-06T09:22:02Z</td>\n",
              "      <td>872.0</td>\n",
              "      <td>Likely Voters</td>\n",
              "      <td>Automated Phone</td>\n",
              "      <td>48.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
              "      <td>http://www.gravispolls.com/2012/11/gravis-mark...</td>\n",
              "      <td>Nonpartisan</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IBD/TIPP</td>\n",
              "      <td>2012-11-03</td>\n",
              "      <td>2012-11-05</td>\n",
              "      <td>2012-11-06T08:51:48Z</td>\n",
              "      <td>712.0</td>\n",
              "      <td>Likely Voters</td>\n",
              "      <td>Live Phone</td>\n",
              "      <td>50.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
              "      <td>http://news.investors.com/special-report/50841...</td>\n",
              "      <td>Nonpartisan</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Rasmussen</td>\n",
              "      <td>2012-11-03</td>\n",
              "      <td>2012-11-05</td>\n",
              "      <td>2012-11-06T08:47:50Z</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>Likely Voters</td>\n",
              "      <td>Automated Phone</td>\n",
              "      <td>48.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
              "      <td>http://www.rasmussenreports.com/public_content...</td>\n",
              "      <td>Nonpartisan</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>JZ Analytics/Newsmax</td>\n",
              "      <td>2012-11-03</td>\n",
              "      <td>2012-11-05</td>\n",
              "      <td>2012-11-06T07:38:41Z</td>\n",
              "      <td>1041.0</td>\n",
              "      <td>Likely Voters</td>\n",
              "      <td>Internet</td>\n",
              "      <td>47.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
              "      <td>http://www.jzanalytics.com/</td>\n",
              "      <td>Sponsor</td>\n",
              "      <td>Rep</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>UPI/CVOTER</td>\n",
              "      <td>2012-11-03</td>\n",
              "      <td>2012-11-05</td>\n",
              "      <td>2012-11-05T18:30:15Z</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>Likely Voters</td>\n",
              "      <td>Live Phone</td>\n",
              "      <td>49.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Nonpartisan</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Ipsos/Reuters (Web)</td>\n",
              "      <td>2012-11-01</td>\n",
              "      <td>2012-11-05</td>\n",
              "      <td>2012-11-05T16:12:48Z</td>\n",
              "      <td>4725.0</td>\n",
              "      <td>Likely Voters</td>\n",
              "      <td>Internet</td>\n",
              "      <td>48.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
              "      <td>http://big.assets.huffingtonpost.com/ipsos1105...</td>\n",
              "      <td>Nonpartisan</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ARG</td>\n",
              "      <td>2012-11-02</td>\n",
              "      <td>2012-11-04</td>\n",
              "      <td>2012-11-05T13:06:54Z</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>Likely Voters</td>\n",
              "      <td>Live Phone</td>\n",
              "      <td>49.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
              "      <td>http://americanresearchgroup.com/pres2012/NA12...</td>\n",
              "      <td>Nonpartisan</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>PPP (D-Americans United for Change)</td>\n",
              "      <td>2012-11-02</td>\n",
              "      <td>2012-11-04</td>\n",
              "      <td>2012-11-04T22:32:19Z</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>Likely Voters</td>\n",
              "      <td>Automated Phone</td>\n",
              "      <td>50.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
              "      <td>http://www.publicpolicypolling.com/pdf/2011/PP...</td>\n",
              "      <td>Sponsor</td>\n",
              "      <td>Dem</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                              Pollster  Start Date    End Date  \\\n",
              "0            Politico/GWU/Battleground  2012-11-04  2012-11-05   \n",
              "1                     YouGov/Economist  2012-11-03  2012-11-05   \n",
              "2                     Gravis Marketing  2012-11-03  2012-11-05   \n",
              "3                             IBD/TIPP  2012-11-03  2012-11-05   \n",
              "4                            Rasmussen  2012-11-03  2012-11-05   \n",
              "5                 JZ Analytics/Newsmax  2012-11-03  2012-11-05   \n",
              "6                           UPI/CVOTER  2012-11-03  2012-11-05   \n",
              "7                  Ipsos/Reuters (Web)  2012-11-01  2012-11-05   \n",
              "8                                  ARG  2012-11-02  2012-11-04   \n",
              "9  PPP (D-Americans United for Change)  2012-11-02  2012-11-04   \n",
              "\n",
              "   Entry Date/Time (ET)  Number of Observations     Population  \\\n",
              "0  2012-11-06T08:40:26Z                  1000.0  Likely Voters   \n",
              "1  2012-11-26T15:31:23Z                   740.0  Likely Voters   \n",
              "2  2012-11-06T09:22:02Z                   872.0  Likely Voters   \n",
              "3  2012-11-06T08:51:48Z                   712.0  Likely Voters   \n",
              "4  2012-11-06T08:47:50Z                  1500.0  Likely Voters   \n",
              "5  2012-11-06T07:38:41Z                  1041.0  Likely Voters   \n",
              "6  2012-11-05T18:30:15Z                  3000.0  Likely Voters   \n",
              "7  2012-11-05T16:12:48Z                  4725.0  Likely Voters   \n",
              "8  2012-11-05T13:06:54Z                  1200.0  Likely Voters   \n",
              "9  2012-11-04T22:32:19Z                  1200.0  Likely Voters   \n",
              "\n",
              "              Mode  Obama  Romney  Undecided  Other  \\\n",
              "0       Live Phone   47.0    47.0        6.0    NaN   \n",
              "1         Internet   49.0    47.0        3.0    NaN   \n",
              "2  Automated Phone   48.0    48.0        4.0    NaN   \n",
              "3       Live Phone   50.0    49.0        NaN    1.0   \n",
              "4  Automated Phone   48.0    49.0        NaN    NaN   \n",
              "5         Internet   47.0    47.0        6.0    NaN   \n",
              "6       Live Phone   49.0    48.0        NaN    NaN   \n",
              "7         Internet   48.0    46.0        4.0    2.0   \n",
              "8       Live Phone   49.0    49.0        1.0    1.0   \n",
              "9  Automated Phone   50.0    48.0        3.0    NaN   \n",
              "\n",
              "                                        Pollster URL  \\\n",
              "0  http://elections.huffingtonpost.com/pollster/p...   \n",
              "1  http://elections.huffingtonpost.com/pollster/p...   \n",
              "2  http://elections.huffingtonpost.com/pollster/p...   \n",
              "3  http://elections.huffingtonpost.com/pollster/p...   \n",
              "4  http://elections.huffingtonpost.com/pollster/p...   \n",
              "5  http://elections.huffingtonpost.com/pollster/p...   \n",
              "6  http://elections.huffingtonpost.com/pollster/p...   \n",
              "7  http://elections.huffingtonpost.com/pollster/p...   \n",
              "8  http://elections.huffingtonpost.com/pollster/p...   \n",
              "9  http://elections.huffingtonpost.com/pollster/p...   \n",
              "\n",
              "                                          Source URL     Partisan Affiliation  \\\n",
              "0  http://www.politico.com/news/stories/1112/8338...  Nonpartisan        None   \n",
              "1  http://cdn.yougov.com/cumulus_uploads/document...  Nonpartisan        None   \n",
              "2  http://www.gravispolls.com/2012/11/gravis-mark...  Nonpartisan        None   \n",
              "3  http://news.investors.com/special-report/50841...  Nonpartisan        None   \n",
              "4  http://www.rasmussenreports.com/public_content...  Nonpartisan        None   \n",
              "5                        http://www.jzanalytics.com/      Sponsor         Rep   \n",
              "6                                                NaN  Nonpartisan        None   \n",
              "7  http://big.assets.huffingtonpost.com/ipsos1105...  Nonpartisan        None   \n",
              "8  http://americanresearchgroup.com/pres2012/NA12...  Nonpartisan        None   \n",
              "9  http://www.publicpolicypolling.com/pdf/2011/PP...      Sponsor         Dem   \n",
              "\n",
              "   Question Text  Question Iteration  \n",
              "0            NaN                   1  \n",
              "1            NaN                   1  \n",
              "2            NaN                   1  \n",
              "3            NaN                   1  \n",
              "4            NaN                   1  \n",
              "5            NaN                   1  \n",
              "6            NaN                   1  \n",
              "7            NaN                   1  \n",
              "8            NaN                   1  \n",
              "9            NaN                   1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "R5BV2B8ALyW5",
        "colab_type": "code",
        "outputId": "ddb89cae-8421-464e-9f9b-f60acfe08bcc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "cell_type": "code",
      "source": [
        "sns.factorplot('Affiliation', data=poll_df, kind='count', size=6)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f270563a6d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AMI2QAQBMI2QAANMIGQDANEIGADDt/wDlNLvY/y9tvwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "HPD_X1Y-NP3A",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Looks like we are overall relatively neutral, but still leaning towards **Democratic Affiliation**, it will be good to keep this in mind. Let's see if sorting by the **Population** hue gives us any further insight into the data."
      ]
    },
    {
      "metadata": {
        "id": "-MYYZclSLyUv",
        "colab_type": "code",
        "outputId": "b8a705ca-8b97-41c1-9c19-e52a9998fe35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "cell_type": "code",
      "source": [
        "# the affiliation by Population\n",
        "sns.factorplot('Affiliation', data=poll_df, kind='count', hue='Population', size=6)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data[hue_mask])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f270354ada0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1JUmSco+XzyRJkjAUSZIkAYYiSZIkwFAkSZIEhDgUffHFFzRs2JDExEQAvvnm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\nT+h7+GRqjgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 584.125x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "MxNJFktANzMh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Looks like we have a strong showing of **Likely Voters** and **Registered Voters**, so the poll data should hopefully be a good reflection on the populations polled. Let's take another quick overview of the DataFrame.\n",
        "\n",
        "Let's go ahead and take a look at the averages for Obama, Romney, and the polled people who remained undecided."
      ]
    },
    {
      "metadata": {
        "id": "mgzdsNqcPZ41",
        "colab_type": "code",
        "outputId": "82a60ae3-ff92-4ab1-84a2-073f9b51dc0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "cell_type": "code",
      "source": [
        "poll_df.describe()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Number of Observations</th>\n",
              "      <th>Obama</th>\n",
              "      <th>Romney</th>\n",
              "      <th>Undecided</th>\n",
              "      <th>Other</th>\n",
              "      <th>Question Text</th>\n",
              "      <th>Question Iteration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>564.000000</td>\n",
              "      <td>586.000000</td>\n",
              "      <td>586.000000</td>\n",
              "      <td>423.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>586.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1296.679078</td>\n",
              "      <td>46.805461</td>\n",
              "      <td>44.614334</td>\n",
              "      <td>6.550827</td>\n",
              "      <td>3.376238</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1621.268369</td>\n",
              "      <td>2.422058</td>\n",
              "      <td>2.906180</td>\n",
              "      <td>3.701754</td>\n",
              "      <td>2.692726</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>328.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>855.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1500.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>46.750000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>36472.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Number of Observations       Obama      Romney   Undecided       Other  \\\n",
              "count              564.000000  586.000000  586.000000  423.000000  202.000000   \n",
              "mean              1296.679078   46.805461   44.614334    6.550827    3.376238   \n",
              "std               1621.268369    2.422058    2.906180    3.701754    2.692726   \n",
              "min                328.000000   37.000000   32.000000    1.000000    0.000000   \n",
              "25%                855.000000   45.000000   43.000000    4.000000    2.000000   \n",
              "50%               1000.000000   47.000000   45.000000    6.000000    3.000000   \n",
              "75%               1500.000000   48.000000   46.750000    8.000000    4.000000   \n",
              "max              36472.000000   54.000000   53.000000   28.000000   19.000000   \n",
              "\n",
              "       Question Text  Question Iteration  \n",
              "count            0.0               586.0  \n",
              "mean             NaN                 1.0  \n",
              "std              NaN                 0.0  \n",
              "min              NaN                 1.0  \n",
              "25%              NaN                 1.0  \n",
              "50%              NaN                 1.0  \n",
              "75%              NaN                 1.0  \n",
              "max              NaN                 1.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "d5LBrCa4LyPO",
        "colab_type": "code",
        "outputId": "6ed77812-4241-482a-9fd2-4ef86a8edac1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "cell_type": "code",
      "source": [
        "avg = pd.DataFrame(poll_df.mean(), columns=['Mean'])\n",
        "avg"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Number of Observations</th>\n",
              "      <td>1296.679078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Obama</th>\n",
              "      <td>46.805461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romney</th>\n",
              "      <td>44.614334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Undecided</th>\n",
              "      <td>6.550827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>3.376238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Question Text</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Question Iteration</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Mean\n",
              "Number of Observations  1296.679078\n",
              "Obama                     46.805461\n",
              "Romney                    44.614334\n",
              "Undecided                  6.550827\n",
              "Other                      3.376238\n",
              "Question Text                   NaN\n",
              "Question Iteration         1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "-rQ-44KALyHl",
        "colab_type": "code",
        "outputId": "7f5a29d8-5565-4b62-e099-b43f017a4cdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "cell_type": "code",
      "source": [
        "std = pd.DataFrame(poll_df.std(), columns=['Standard Deviation'])\n",
        "std"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Standard Deviation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Number of Observations</th>\n",
              "      <td>1621.268369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Obama</th>\n",
              "      <td>2.422058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romney</th>\n",
              "      <td>2.906180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Undecided</th>\n",
              "      <td>3.701754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>2.692726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Question Text</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Question Iteration</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Standard Deviation\n",
              "Number of Observations         1621.268369\n",
              "Obama                             2.422058\n",
              "Romney                            2.906180\n",
              "Undecided                         3.701754\n",
              "Other                             2.692726\n",
              "Question Text                          NaN\n",
              "Question Iteration                0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "2Ao5sVbtOtSk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# drop the Number of Observations' row in both DataFrames\n",
        "std.drop('Number of Observations', axis=0, inplace=True)\n",
        "avg.drop('Number of Observations', axis=0, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Mj96ekVFORAE",
        "colab_type": "code",
        "outputId": "4ea40ef5-2b50-4d50-b5d9-56389573d6de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        }
      },
      "cell_type": "code",
      "source": [
        "# plot the result by using pandas built-in plot function with kind='bar' and set the y error, yerr='std'\n",
        "# set the y error ()\n",
        "avg.plot(yerr=std, kind='bar', legend=False, figsize=(15,6))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2703437630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XdOvWLUuWLMnUqVPz0ksv5aqrriodjwq0dFv0v54AbAs9y7PJJpukf//+2Xbb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\nAAAU9P8B7mFAx7PsLe4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "WgKntpdlPzMN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "This is the average sentiment of all the polls. Interesting to see how close these polls seem to be, especially considering the undecided factor. Let's take a look at the numbers."
      ]
    },
    {
      "metadata": {
        "id": "xN5DN79pOnjG",
        "colab_type": "code",
        "outputId": "80822359-d287-4606-ab15-3ed795793c42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "cell_type": "code",
      "source": [
        "# concatenate the two dataframes (mean and std)\n",
        "poll_avg = pd.concat([avg, std], axis=1)\n",
        "poll_avg.columns = ['Average', 'STD']\n",
        "poll_avg"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Average</th>\n",
              "      <th>STD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Obama</th>\n",
              "      <td>46.805461</td>\n",
              "      <td>2.422058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romney</th>\n",
              "      <td>44.614334</td>\n",
              "      <td>2.906180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Undecided</th>\n",
              "      <td>6.550827</td>\n",
              "      <td>3.701754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>3.376238</td>\n",
              "      <td>2.692726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Question Text</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Question Iteration</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Average       STD\n",
              "Obama               46.805461  2.422058\n",
              "Romney              44.614334  2.906180\n",
              "Undecided            6.550827  3.701754\n",
              "Other                3.376238  2.692726\n",
              "Question Text             NaN       NaN\n",
              "Question Iteration   1.000000  0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "tqkNoj3uQlMd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Looks like the polls indicate it as a fairly close race, but what about the undecided voters? Most of them will likely vote for one of the candidates once the election occurs. If we assume we split the undecided evenly between the two candidates, the observed difference should be an unbiased estimate of the final difference. If we wanted to, we could also do a quick time series analysis of the voter sentiment by plotting Obama/Romney favor versus the Poll End Dates. Let's take a look at how we could quickly do that in pandas. \n",
        "\n",
        "**Note:** The time is in reverse chronological order. Also keep in mind the multiple polls per end date."
      ]
    },
    {
      "metadata": {
        "id": "a_tqGBGHPrtW",
        "colab_type": "code",
        "outputId": "1e18d508-6e8b-4249-992a-8e3946281bc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "cell_type": "code",
      "source": [
        "# plot of sentiment in the polls versus time.\n",
        "poll_df.plot(x='End Date', y=['Obama','Romney','Undecided'], linestyle='', marker='o', figsize=(15,6), xticks=(0,200, 400, 500))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/plotting/_core.py:1716: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
            "  series.name = label\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2703409860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xl3/5l/D7/UQbbhT6kA8EeptjCNz4gW172QtbyzZpGpzqX6YntBEj6Utl15/e9UUUpjpK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\nRERERGSj/w/HcbioiP7xlgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "HSl2sllnS00M",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We can see the quick time analysis of voter sentiment for each of the candidates and undecided voters. In the x axis, on the right hand side, there are the earlier dates and on the left hand side are the later dates. Trend of undecided voters starts to go down as you get closer to elections. And the other interesting thing is Obama and Romney are getting closer and closer on the left hand side. While this may give you a quick idea, go ahead and try creating a new DataFrame or editing poll_df to make a better visualization of the above idea. Let's go ahead and plot out the difference between Obama and Romney and how it changes as time moves along."
      ]
    },
    {
      "metadata": {
        "id": "QFHTujnWSNhW",
        "colab_type": "code",
        "outputId": "e282834b-48c5-4572-a3dd-286d4b661a74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "poll_df[['Obama','Romney']].head(5)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Obama</th>\n",
              "      <th>Romney</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>47.0</td>\n",
              "      <td>47.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49.0</td>\n",
              "      <td>47.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>48.0</td>\n",
              "      <td>48.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50.0</td>\n",
              "      <td>49.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>48.0</td>\n",
              "      <td>49.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Obama  Romney\n",
              "0   47.0    47.0\n",
              "1   49.0    47.0\n",
              "2   48.0    48.0\n",
              "3   50.0    49.0\n",
              "4   48.0    49.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "W9luSsqwSz4u",
        "colab_type": "code",
        "outputId": "95153434-c91d-42af-c888-804cfddf532a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "poll_df['Difference Percentage'] = (poll_df.Obama - poll_df.Romney) / 100 # percentage points\n",
        "poll_df[['Obama','Romney','Difference Percentage']].head(5)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Obama</th>\n",
              "      <th>Romney</th>\n",
              "      <th>Difference Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>47.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>48.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>48.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>-0.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Obama  Romney  Difference Percentage\n",
              "0   47.0    47.0                   0.00\n",
              "1   49.0    47.0                   0.02\n",
              "2   48.0    48.0                   0.00\n",
              "3   50.0    49.0                   0.01\n",
              "4   48.0    49.0                  -0.01"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "c74mFsnFT7ce",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Positive difference** indicates a leaning towards Obama in the polls.\n",
        "**Negative difference** indicates a leaning towards Romney in the polls.\n",
        "\n",
        "Now let's go ahead and see if we can visualize how this sentiment in difference changes over time. We will start by using groupby to group the polls by their start data and then sorting it by that Start Date."
      ]
    },
    {
      "metadata": {
        "id": "0336nTleTd3u",
        "colab_type": "code",
        "outputId": "88862442-a83e-47f9-a3f6-21187f68c90d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "poll_df = poll_df.groupby(['Start Date'], as_index=False).mean()\n",
        "poll_df.head(5)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Start Date</th>\n",
              "      <th>Number of Observations</th>\n",
              "      <th>Obama</th>\n",
              "      <th>Romney</th>\n",
              "      <th>Undecided</th>\n",
              "      <th>Other</th>\n",
              "      <th>Question Text</th>\n",
              "      <th>Question Iteration</th>\n",
              "      <th>Difference Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-03-13</td>\n",
              "      <td>1403.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-04-17</td>\n",
              "      <td>686.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-05-14</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-06-12</td>\n",
              "      <td>638.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-07-15</td>\n",
              "      <td>577.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Start Date  Number of Observations  Obama  Romney  Undecided  Other  \\\n",
              "0  2009-03-13                  1403.0   44.0    44.0       12.0    NaN   \n",
              "1  2009-04-17                   686.0   50.0    39.0       11.0    NaN   \n",
              "2  2009-05-14                  1000.0   53.0    35.0       12.0    NaN   \n",
              "3  2009-06-12                   638.0   48.0    40.0       12.0    NaN   \n",
              "4  2009-07-15                   577.0   49.0    40.0       11.0    NaN   \n",
              "\n",
              "   Question Text  Question Iteration  Difference Percentage  \n",
              "0            NaN                   1                   0.00  \n",
              "1            NaN                   1                   0.11  \n",
              "2            NaN                   1                   0.18  \n",
              "3            NaN                   1                   0.08  \n",
              "4            NaN                   1                   0.09  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "KgTVlUzKWvq9",
        "colab_type": "code",
        "outputId": "be8fe31a-d5d2-4810-cdaa-7ec83e6f60cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "cell_type": "code",
      "source": [
        "poll_df.plot(x='Start Date', y='Difference Percentage', figsize=(15,6), marker='o', linestyle='-', color='green', xticks=(25,130,240,330))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f27030a8c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rK89fqfr9nefdOcwlSS+Fel0EQRBE+qC+giCIXICMuRRQ5pkDMKJcLb0eL9ZdtQ5uuxuA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\nAMCGCOYAAAAAwIYI5gAAAADAhv4/uiKcLCW40YcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "1aqpfjRZeneK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Looks like Obama was in the lead for most of the year. Because if the difference is minus, then it means Romney was leading. \n",
        "\n",
        "It would be very interesting to plot marker lines on the dates of the debates and see if there is any general insight to the poll results. The debate dates were Oct 3rd, Oct 11, and Oct 22nd. Let's plot some lines as markers and then zoom in on the month of October. In order to find where to set the x limits for the figure, we need to find out where the index for the month of October in 2012 is. Here's a simple for loop to find that row. Note, the string format of the date makes this difficult to do without using a lambda expression or a map."
      ]
    },
    {
      "metadata": {
        "id": "hc5zDk7vcNU9",
        "colab_type": "code",
        "outputId": "1f87b63a-0087-472c-9f98-1b64d6d14077",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# set row count and xlimit list\n",
        "row_in = 0 # row_index\n",
        "xlimit = []\n",
        "\n",
        "for date in poll_df['Start Date']:\n",
        "    if date[0:7] == '2012-10':\n",
        "        xlimit.append(row_in)\n",
        "        row_in += 1\n",
        "    else:\n",
        "        row_in += 1\n",
        "        \n",
        "min_xlimit = min(xlimit)\n",
        "max_xlimit = max(xlimit)\n",
        "print(xlimit)\n",
        "print(min_xlimit, max_xlimit)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352]\n",
            "325 352\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "JkoXWRyfkrby",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The debate dates were Oct 3rd, Oct 11th, and Oct 22nd. So, now we are going to plot some lines on top of this plot, and see where we can find the markers of the debates and any chances to the sentiment of voters."
      ]
    },
    {
      "metadata": {
        "id": "mkOhS42hkaes",
        "colab_type": "code",
        "outputId": "585affcc-1649-4f70-b8ca-1771a1a0adc6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "cell_type": "code",
      "source": [
        "poll_df.plot(x='Start Date', y='Difference Percentage', \n",
        "             figsize=(15,6), marker='o', linestyle='-', \n",
        "             color='green', xlim=(min_xlimit, max_xlimit),\n",
        "             xticks=(min_xlimit+1, min_xlimit+2, min_xlimit+8, min_xlimit+18),\n",
        "             )\n",
        "\n",
        "plt.xticks(rotation='vertical')\n",
        "\n",
        "# add the debate markers for Oct 2nd, Oct 4th, Oct 11th and Oct 22nd\n",
        "# the reason why we plot Oct 2nd and Oct 4th instead of Oct 3rd is the data belonging to the first debate date is not recorded\n",
        "\n",
        "# Oct 2nd\n",
        "plt.axvline(min_xlimit+1, linewidth=4, color='grey')\n",
        "\n",
        "# Oct 4th\n",
        "plt.axvline(min_xlimit+2, linewidth=4, color='grey')\n",
        "\n",
        "# Oct 11th\n",
        "plt.axvline(min_xlimit+8, linewidth=4, color='grey')\n",
        "\n",
        "# Oct 22nd\n",
        "plt.axvline(min_xlimit+18, linewidth=4, color='grey')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7f2702051860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fv1N8UrzsbOz0fJ3n9bbP2/Kt6Gv4/9dZZaJsnzFNnjxZBw8elMlk0qhRo+Tl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\n+g+u87Vy/YZR0wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "urGWw0vioIvA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "After the first debate, sentiment for Obama increased, and after the second debate Romney was more favorable, after the third debate every ones were pretty even.\n",
        "\n",
        "For all these polls, it is important to remember how geographical location can effect the value of a poll in predicting the outcomes of a national election."
      ]
    },
    {
      "metadata": {
        "id": "-_TXCZqSrtyc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Donor Data Set\n",
        "\n",
        "Let's take a look at a data set consisting of information on donations to the federal campaign. You can download it [here](https://www.dropbox.com/s/l29oppon2veaq4n/Election_Donor_Data.csv?dl=0).\n",
        "\n",
        "The questions we will be trying to answer while looking at this data set is:\n",
        "\n",
        "    1. How much was donated and what was the average donation?\n",
        "    2. How did the donations differ between candidates?\n",
        "    3. How did the donations differ between Democrats and Republicans?\n",
        "    4. What were the demographics of the donors?\n",
        "    5. Is there a pattern to donation amounts?"
      ]
    },
    {
      "metadata": {
        "id": "QJE9qFfNyyb-",
        "colab_type": "code",
        "outputId": "de9b71a6-fd1b-42c4-fc82-34567a7f9264",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "\"\"\"from google.colab import files\n",
        "uploaded = files.upload()\"\"\""
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c8d619b1-880d-4aac-8b41-9b6cf6209085\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-c8d619b1-880d-4aac-8b41-9b6cf6209085\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Election_Donor_Data.csv to Election_Donor_Data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jIaXVPCvYUGt",
        "colab_type": "code",
        "outputId": "61876787-56a3-4b9f-c825-be179b2e6660",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "cell_type": "code",
      "source": [
        "donor_df = pd.read_csv('Election_Donor_Data.csv', low_memory=False)\n",
        "donor_df.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cmte_id</th>\n",
              "      <th>cand_id</th>\n",
              "      <th>cand_nm</th>\n",
              "      <th>contbr_nm</th>\n",
              "      <th>contbr_city</th>\n",
              "      <th>contbr_st</th>\n",
              "      <th>contbr_zip</th>\n",
              "      <th>contbr_employer</th>\n",
              "      <th>contbr_occupation</th>\n",
              "      <th>contb_receipt_amt</th>\n",
              "      <th>contb_receipt_dt</th>\n",
              "      <th>receipt_desc</th>\n",
              "      <th>memo_cd</th>\n",
              "      <th>memo_text</th>\n",
              "      <th>form_tp</th>\n",
              "      <th>file_num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>C00410118</td>\n",
              "      <td>P20002978</td>\n",
              "      <td>Bachmann, Michelle</td>\n",
              "      <td>HARVEY, WILLIAM</td>\n",
              "      <td>MOBILE</td>\n",
              "      <td>AL</td>\n",
              "      <td>366010290</td>\n",
              "      <td>RETIRED</td>\n",
              "      <td>RETIRED</td>\n",
              "      <td>250.0</td>\n",
              "      <td>20-JUN-11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SA17A</td>\n",
              "      <td>736166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>C00410118</td>\n",
              "      <td>P20002978</td>\n",
              "      <td>Bachmann, Michelle</td>\n",
              "      <td>HARVEY, WILLIAM</td>\n",
              "      <td>MOBILE</td>\n",
              "      <td>AL</td>\n",
              "      <td>366010290</td>\n",
              "      <td>RETIRED</td>\n",
              "      <td>RETIRED</td>\n",
              "      <td>50.0</td>\n",
              "      <td>23-JUN-11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SA17A</td>\n",
              "      <td>736166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>C00410118</td>\n",
              "      <td>P20002978</td>\n",
              "      <td>Bachmann, Michelle</td>\n",
              "      <td>SMITH, LANIER</td>\n",
              "      <td>LANETT</td>\n",
              "      <td>AL</td>\n",
              "      <td>368633403</td>\n",
              "      <td>INFORMATION REQUESTED</td>\n",
              "      <td>INFORMATION REQUESTED</td>\n",
              "      <td>250.0</td>\n",
              "      <td>05-JUL-11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SA17A</td>\n",
              "      <td>749073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>C00410118</td>\n",
              "      <td>P20002978</td>\n",
              "      <td>Bachmann, Michelle</td>\n",
              "      <td>BLEVINS, DARONDA</td>\n",
              "      <td>PIGGOTT</td>\n",
              "      <td>AR</td>\n",
              "      <td>724548253</td>\n",
              "      <td>NONE</td>\n",
              "      <td>RETIRED</td>\n",
              "      <td>250.0</td>\n",
              "      <td>01-AUG-11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SA17A</td>\n",
              "      <td>749073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>C00410118</td>\n",
              "      <td>P20002978</td>\n",
              "      <td>Bachmann, Michelle</td>\n",
              "      <td>WARDENBURG, HAROLD</td>\n",
              "      <td>HOT SPRINGS NATION</td>\n",
              "      <td>AR</td>\n",
              "      <td>719016467</td>\n",
              "      <td>NONE</td>\n",
              "      <td>RETIRED</td>\n",
              "      <td>300.0</td>\n",
              "      <td>20-JUN-11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SA17A</td>\n",
              "      <td>736166</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     cmte_id    cand_id             cand_nm           contbr_nm  \\\n",
              "0  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
              "1  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
              "2  C00410118  P20002978  Bachmann, Michelle       SMITH, LANIER   \n",
              "3  C00410118  P20002978  Bachmann, Michelle    BLEVINS, DARONDA   \n",
              "4  C00410118  P20002978  Bachmann, Michelle  WARDENBURG, HAROLD   \n",
              "\n",
              "          contbr_city contbr_st contbr_zip        contbr_employer  \\\n",
              "0              MOBILE        AL  366010290                RETIRED   \n",
              "1              MOBILE        AL  366010290                RETIRED   \n",
              "2              LANETT        AL  368633403  INFORMATION REQUESTED   \n",
              "3             PIGGOTT        AR  724548253                   NONE   \n",
              "4  HOT SPRINGS NATION        AR  719016467                   NONE   \n",
              "\n",
              "       contbr_occupation  contb_receipt_amt contb_receipt_dt receipt_desc  \\\n",
              "0                RETIRED              250.0        20-JUN-11          NaN   \n",
              "1                RETIRED               50.0        23-JUN-11          NaN   \n",
              "2  INFORMATION REQUESTED              250.0        05-JUL-11          NaN   \n",
              "3                RETIRED              250.0        01-AUG-11          NaN   \n",
              "4                RETIRED              300.0        20-JUN-11          NaN   \n",
              "\n",
              "  memo_cd memo_text form_tp  file_num  \n",
              "0     NaN       NaN   SA17A    736166  \n",
              "1     NaN       NaN   SA17A    736166  \n",
              "2     NaN       NaN   SA17A    749073  \n",
              "3     NaN       NaN   SA17A    749073  \n",
              "4     NaN       NaN   SA17A    736166  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "pWG3CPdd52Nr",
        "colab_type": "code",
        "outputId": "9fcc7aff-c81a-45e6-c9ff-45b5e17978fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "cell_type": "code",
      "source": [
        "donor_df.info()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1001731 entries, 0 to 1001730\n",
            "Data columns (total 16 columns):\n",
            "cmte_id              1001731 non-null object\n",
            "cand_id              1001731 non-null object\n",
            "cand_nm              1001731 non-null object\n",
            "contbr_nm            1001731 non-null object\n",
            "contbr_city          1001712 non-null object\n",
            "contbr_st            1001727 non-null object\n",
            "contbr_zip           1001620 non-null object\n",
            "contbr_employer      988002 non-null object\n",
            "contbr_occupation    993301 non-null object\n",
            "contb_receipt_amt    1001731 non-null float64\n",
            "contb_receipt_dt     1001731 non-null object\n",
            "receipt_desc         14166 non-null object\n",
            "memo_cd              92482 non-null object\n",
            "memo_text            97770 non-null object\n",
            "form_tp              1001731 non-null object\n",
            "file_num             1001731 non-null int64\n",
            "dtypes: float64(1), int64(1), object(14)\n",
            "memory usage: 122.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "UHMV8DkLAnw7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Get a quick glimpse of the donation amounts, and the average donation amount"
      ]
    },
    {
      "metadata": {
        "id": "ayNpCoN3AVor",
        "colab_type": "code",
        "outputId": "69df5734-350f-482d-8d0c-6de1cde85369",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1071
        }
      },
      "cell_type": "code",
      "source": [
        "# contb_receipt_amt -> contributor receipt amount\n",
        "donor_df['contb_receipt_amt'].value_counts()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 100.00     178188\n",
              " 50.00      137584\n",
              " 25.00      110345\n",
              " 250.00      91182\n",
              " 500.00      57984\n",
              " 2500.00     49005\n",
              " 35.00       37237\n",
              " 1000.00     36494\n",
              " 10.00       33986\n",
              " 200.00      27813\n",
              " 20.00       17565\n",
              " 15.00       16163\n",
              " 150.00      14600\n",
              " 75.00       13647\n",
              " 201.20      11718\n",
              " 30.00       11381\n",
              " 300.00      11204\n",
              " 20.12        9897\n",
              " 5.00         9024\n",
              " 40.00        5007\n",
              " 2000.00      4128\n",
              " 55.00        3760\n",
              " 1500.00      3705\n",
              " 3.00         3383\n",
              " 60.00        3084\n",
              " 400.00       3066\n",
              "-2500.00      2727\n",
              " 110.00       2554\n",
              " 125.00       2520\n",
              " 19.00        2474\n",
              "             ...  \n",
              " 174.80          1\n",
              " 7.27            1\n",
              " 1219.00         1\n",
              " 1884.88         1\n",
              " 162.25          1\n",
              " 218.31          1\n",
              " 78.62           1\n",
              " 203.16          1\n",
              " 53.11           1\n",
              " 499.66          1\n",
              " 19.53           1\n",
              " 188.60          1\n",
              " 47.10           1\n",
              " 19.85           1\n",
              " 28.83           1\n",
              " 202.59          1\n",
              "-5500.00         1\n",
              " 9.25            1\n",
              " 202.66          1\n",
              " 1205.00         1\n",
              " 80.73           1\n",
              " 115.07          1\n",
              " 213.69          1\n",
              " 70.76           1\n",
              " 144.13          1\n",
              " 97.15           1\n",
              " 122.32          1\n",
              " 188.65          1\n",
              " 122.40          1\n",
              " 132.12          1\n",
              "Name: contb_receipt_amt, Length: 8079, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "metadata": {
        "id": "NbY-gBJaA3Xm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**value_counts** basically counts how many times each unique value occurs. This means most common donation was 100$ donation. 178188 people donate 100 dolars. In total, there are 8079 different amounts. "
      ]
    },
    {
      "metadata": {
        "id": "NeAL2t8NA8HD",
        "colab_type": "code",
        "outputId": "908607df-44dd-4c49-e03f-ba1837c9d4f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "don_mean = donor_df['contb_receipt_amt'].mean()\n",
        "don_std = donor_df['contb_receipt_amt'].std()\n",
        "print('The average donation was %.2f with a std of %.2f' %(don_mean, don_std))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The average donation was 298.24 with a std of 3749.67\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "X-DMAUOjBNaa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**3749.67** is a huge standard deviation. Let's see if there are any large donations or other factors messing with the distribution of the donations."
      ]
    },
    {
      "metadata": {
        "id": "sWba565TBBqj",
        "colab_type": "code",
        "outputId": "9fc5d22a-e391-42d9-9cb7-f4780d8a189d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1071
        }
      },
      "cell_type": "code",
      "source": [
        "top_donor = donor_df['contb_receipt_amt'].copy()\n",
        "top_donor.sort_values(ascending=True)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "114604     -30800.00\n",
              "226986     -25800.00\n",
              "101356      -7500.00\n",
              "398429      -5500.00\n",
              "250737      -5455.00\n",
              "33821       -5414.31\n",
              "908565      -5115.00\n",
              "456649      -5000.00\n",
              "574657      -5000.00\n",
              "30513       -5000.00\n",
              "562267      -5000.00\n",
              "30584       -5000.00\n",
              "86268       -5000.00\n",
              "708920      -5000.00\n",
              "665887      -5000.00\n",
              "708899      -5000.00\n",
              "708929      -5000.00\n",
              "21172       -5000.00\n",
              "21168       -5000.00\n",
              "21167       -5000.00\n",
              "262328      -5000.00\n",
              "946875      -5000.00\n",
              "7361        -5000.00\n",
              "416403      -5000.00\n",
              "21164       -5000.00\n",
              "707945      -5000.00\n",
              "615101      -5000.00\n",
              "7973        -5000.00\n",
              "54430       -5000.00\n",
              "54434       -5000.00\n",
              "             ...    \n",
              "708022      10000.00\n",
              "708898      10000.00\n",
              "710177      10000.00\n",
              "876244      10000.00\n",
              "709608      10000.00\n",
              "708919      10000.00\n",
              "709739      10000.00\n",
              "91145       10000.00\n",
              "708138      10000.00\n",
              "993178      10000.00\n",
              "709813      10000.00\n",
              "710730      10000.00\n",
              "708928      10000.00\n",
              "709268      10000.00\n",
              "99829       10000.00\n",
              "90076       10000.00\n",
              "709859      10000.00\n",
              "41888       10000.00\n",
              "65131       12700.00\n",
              "834301      25000.00\n",
              "823345      25000.00\n",
              "217891      25800.00\n",
              "114754      33300.00\n",
              "257270     451726.00\n",
              "335187     512710.91\n",
              "319478     526246.17\n",
              "344419    1511192.17\n",
              "344539    1679114.65\n",
              "326651    1944042.43\n",
              "325136    2014490.51\n",
              "Name: contb_receipt_amt, Length: 1001731, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "b8yHGE2YB0Y8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Looks like we have some negative values, as well as some huge donation amounts. The negative values are due to the FEC (Federal Election Commission) recording refunds as well as donations, let's go ahead and only look at the positive contribution amounts."
      ]
    },
    {
      "metadata": {
        "id": "gf_gaK0YBohI",
        "colab_type": "code",
        "outputId": "fef639a7-19a0-4e0c-f024-0503c3a30540",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "# get rid of the negative values\n",
        "top_donor = top_donor[top_donor > 0]\n",
        "top_donor.sort_values(ascending=True).value_counts().head(10)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100.0     178188\n",
              "50.0      137584\n",
              "25.0      110345\n",
              "250.0      91182\n",
              "500.0      57984\n",
              "2500.0     49005\n",
              "35.0       37237\n",
              "1000.0     36494\n",
              "10.0       33986\n",
              "200.0      27813\n",
              "Name: contb_receipt_amt, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "AFYLPLjVB8-5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "10 most common donations ranged from 10 to 2500 dollars. A quick question we could verify is if donations are usually made in round number amounts? (e.g. 10,20,50,100,500 etc.) We can quickly visualize this by making a histogram and checking for peaks at those values. Let's go ahead and do this for the most common amounts, up to 2500 dollars."
      ]
    },
    {
      "metadata": {
        "id": "Xo7yQpueB6LJ",
        "colab_type": "code",
        "outputId": "364b7bea-2ac8-4081-95cc-8e374068a8d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        }
      },
      "cell_type": "code",
      "source": [
        "com_don = top_donor[top_donor <= 2500]\n",
        "com_don.hist(bins=100, figsize=(15,8))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f26fcddd278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x+2CJAAAAB6c+B9/fPmPvb6ZMmZIpU6bscNzMmTMzc+bM7cYOPfTQfPe7393h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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "o5-jYAt1IJLA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's dive deeper into the data and see if we can seperate donations by Party, in order to do this we'll have to figure out a way of creating a new 'Party' column. We can do this by starting with the candidates and their affliliation. Now let's go ahead and get a list of candidates."
      ]
    },
    {
      "metadata": {
        "id": "7YucV_MyILPK",
        "colab_type": "code",
        "outputId": "2a6036ca-c6a5-4bdc-af75-401d05809308",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "cell_type": "code",
      "source": [
        "# grab the unique object from the candidate column\n",
        "# cand_nm -> candidate name\n",
        "candidates = donor_df.cand_nm.unique()\n",
        "candidates"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Bachmann, Michelle', 'Romney, Mitt', 'Obama, Barack',\n",
              "       \"Roemer, Charles E. 'Buddy' III\", 'Pawlenty, Timothy',\n",
              "       'Johnson, Gary Earl', 'Paul, Ron', 'Santorum, Rick',\n",
              "       'Cain, Herman', 'Gingrich, Newt', 'McCotter, Thaddeus G',\n",
              "       'Huntsman, Jon', 'Perry, Rick'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "metadata": {
        "id": "9iRwrw-7IMca",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Dictionary of party affiliation\n",
        "party_map = {'Bachmann, Michelle': 'Republican',\n",
        "           'Cain, Herman': 'Republican',\n",
        "           'Gingrich, Newt': 'Republican',\n",
        "           'Huntsman, Jon': 'Republican',\n",
        "           'Johnson, Gary Earl': 'Republican',\n",
        "           'McCotter, Thaddeus G': 'Republican',\n",
        "           'Obama, Barack': 'Democrat',\n",
        "           'Paul, Ron': 'Republican',\n",
        "           'Pawlenty, Timothy': 'Republican',\n",
        "           'Perry, Rick': 'Republican',\n",
        "           \"Roemer, Charles E. 'Buddy' III\": 'Republican',\n",
        "           'Romney, Mitt': 'Republican',\n",
        "           'Santorum, Rick': 'Republican'}\n",
        "\n",
        "# Now map the party with candidate\n",
        "donor_df['Party'] = donor_df.cand_nm.map(party_map)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_fc2zrObOOdp",
        "colab_type": "code",
        "outputId": "1442032a-0d54-4954-da77-184f2a431403",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "cell_type": "code",
      "source": [
        "donor_df[['cand_nm','Party']][1:1001731:100000]"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cand_nm</th>\n",
              "      <th>Party</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bachmann, Michelle</td>\n",
              "      <td>Republican</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100001</th>\n",
              "      <td>Romney, Mitt</td>\n",
              "      <td>Republican</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200001</th>\n",
              "      <td>Obama, Barack</td>\n",
              "      <td>Democrat</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300001</th>\n",
              "      <td>Obama, Barack</td>\n",
              "      <td>Democrat</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>400001</th>\n",
              "      <td>Obama, Barack</td>\n",
              "      <td>Democrat</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500001</th>\n",
              "      <td>Obama, Barack</td>\n",
              "      <td>Democrat</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>600001</th>\n",
              "      <td>Obama, Barack</td>\n",
              "      <td>Democrat</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>700001</th>\n",
              "      <td>Obama, Barack</td>\n",
              "      <td>Democrat</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>800001</th>\n",
              "      <td>Paul, Ron</td>\n",
              "      <td>Republican</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>900001</th>\n",
              "      <td>Santorum, Rick</td>\n",
              "      <td>Republican</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1000001</th>\n",
              "      <td>Perry, Rick</td>\n",
              "      <td>Republican</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    cand_nm       Party\n",
              "1        Bachmann, Michelle  Republican\n",
              "100001         Romney, Mitt  Republican\n",
              "200001        Obama, Barack    Democrat\n",
              "300001        Obama, Barack    Democrat\n",
              "400001        Obama, Barack    Democrat\n",
              "500001        Obama, Barack    Democrat\n",
              "600001        Obama, Barack    Democrat\n",
              "700001        Obama, Barack    Democrat\n",
              "800001            Paul, Ron  Republican\n",
              "900001       Santorum, Rick  Republican\n",
              "1000001         Perry, Rick  Republican"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "id": "K9qZ4HY5EcKI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's look at our DataFrame and also make sure we clear refunds from the contribution amounts."
      ]
    },
    {
      "metadata": {
        "id": "6ZdnNzEXOQLg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "d773cf17-a71c-4f5d-8cf2-ba2f1c669b7e"
      },
      "cell_type": "code",
      "source": [
        "# contb_receipt_amt -> contribution receipt amount\n",
        "# clear refunds\n",
        "donor_df = donor_df[donor_df.contb_receipt_amt > 0]\n",
        "donor_df.head(5)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cmte_id</th>\n",
              "      <th>cand_id</th>\n",
              "      <th>cand_nm</th>\n",
              "      <th>contbr_nm</th>\n",
              "      <th>contbr_city</th>\n",
              "      <th>contbr_st</th>\n",
              "      <th>contbr_zip</th>\n",
              "      <th>contbr_employer</th>\n",
              "      <th>contbr_occupation</th>\n",
              "      <th>contb_receipt_amt</th>\n",
              "      <th>contb_receipt_dt</th>\n",
              "      <th>receipt_desc</th>\n",
              "      <th>memo_cd</th>\n",
              "      <th>memo_text</th>\n",
              "      <th>form_tp</th>\n",
              "      <th>file_num</th>\n",
              "      <th>Party</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>C00410118</td>\n",
              "      <td>P20002978</td>\n",
              "      <td>Bachmann, Michelle</td>\n",
              "      <td>HARVEY, WILLIAM</td>\n",
              "      <td>MOBILE</td>\n",
              "      <td>AL</td>\n",
              "      <td>366010290</td>\n",
              "      <td>RETIRED</td>\n",
              "      <td>RETIRED</td>\n",
              "      <td>250.0</td>\n",
              "      <td>20-JUN-11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SA17A</td>\n",
              "      <td>736166</td>\n",
              "      <td>Republican</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>C00410118</td>\n",
              "      <td>P20002978</td>\n",
              "      <td>Bachmann, Michelle</td>\n",
              "      <td>HARVEY, WILLIAM</td>\n",
              "      <td>MOBILE</td>\n",
              "      <td>AL</td>\n",
              "      <td>366010290</td>\n",
              "      <td>RETIRED</td>\n",
              "      <td>RETIRED</td>\n",
              "      <td>50.0</td>\n",
              "      <td>23-JUN-11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SA17A</td>\n",
              "      <td>736166</td>\n",
              "      <td>Republican</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>C00410118</td>\n",
              "      <td>P20002978</td>\n",
              "      <td>Bachmann, Michelle</td>\n",
              "      <td>SMITH, LANIER</td>\n",
              "      <td>LANETT</td>\n",
              "      <td>AL</td>\n",
              "      <td>368633403</td>\n",
              "      <td>INFORMATION REQUESTED</td>\n",
              "      <td>INFORMATION REQUESTED</td>\n",
              "      <td>250.0</td>\n",
              "      <td>05-JUL-11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SA17A</td>\n",
              "      <td>749073</td>\n",
              "      <td>Republican</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>C00410118</td>\n",
              "      <td>P20002978</td>\n",
              "      <td>Bachmann, Michelle</td>\n",
              "      <td>BLEVINS, DARONDA</td>\n",
              "      <td>PIGGOTT</td>\n",
              "      <td>AR</td>\n",
              "      <td>724548253</td>\n",
              "      <td>NONE</td>\n",
              "      <td>RETIRED</td>\n",
              "      <td>250.0</td>\n",
              "      <td>01-AUG-11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SA17A</td>\n",
              "      <td>749073</td>\n",
              "      <td>Republican</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>C00410118</td>\n",
              "      <td>P20002978</td>\n",
              "      <td>Bachmann, Michelle</td>\n",
              "      <td>WARDENBURG, HAROLD</td>\n",
              "      <td>HOT SPRINGS NATION</td>\n",
              "      <td>AR</td>\n",
              "      <td>719016467</td>\n",
              "      <td>NONE</td>\n",
              "      <td>RETIRED</td>\n",
              "      <td>300.0</td>\n",
              "      <td>20-JUN-11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SA17A</td>\n",
              "      <td>736166</td>\n",
              "      <td>Republican</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     cmte_id    cand_id             cand_nm           contbr_nm  \\\n",
              "0  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
              "1  C00410118  P20002978  Bachmann, Michelle     HARVEY, WILLIAM   \n",
              "2  C00410118  P20002978  Bachmann, Michelle       SMITH, LANIER   \n",
              "3  C00410118  P20002978  Bachmann, Michelle    BLEVINS, DARONDA   \n",
              "4  C00410118  P20002978  Bachmann, Michelle  WARDENBURG, HAROLD   \n",
              "\n",
              "          contbr_city contbr_st contbr_zip        contbr_employer  \\\n",
              "0              MOBILE        AL  366010290                RETIRED   \n",
              "1              MOBILE        AL  366010290                RETIRED   \n",
              "2              LANETT        AL  368633403  INFORMATION REQUESTED   \n",
              "3             PIGGOTT        AR  724548253                   NONE   \n",
              "4  HOT SPRINGS NATION        AR  719016467                   NONE   \n",
              "\n",
              "       contbr_occupation  contb_receipt_amt contb_receipt_dt receipt_desc  \\\n",
              "0                RETIRED              250.0        20-JUN-11          NaN   \n",
              "1                RETIRED               50.0        23-JUN-11          NaN   \n",
              "2  INFORMATION REQUESTED              250.0        05-JUL-11          NaN   \n",
              "3                RETIRED              250.0        01-AUG-11          NaN   \n",
              "4                RETIRED              300.0        20-JUN-11          NaN   \n",
              "\n",
              "  memo_cd memo_text form_tp  file_num       Party  \n",
              "0     NaN       NaN   SA17A    736166  Republican  \n",
              "1     NaN       NaN   SA17A    736166  Republican  \n",
              "2     NaN       NaN   SA17A    749073  Republican  \n",
              "3     NaN       NaN   SA17A    749073  Republican  \n",
              "4     NaN       NaN   SA17A    736166  Republican  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "id": "-Q2Plpw4EhUy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's start by aggregating the data by candidate. We'll take a quick look a the total amounts received by each candidate. First we will look at the total number of donations and then at the total amount."
      ]
    },
    {
      "metadata": {
        "id": "ESqp5_6PEh9p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "063802ea-f28a-42b1-be17-d36b7a76b180"
      },
      "cell_type": "code",
      "source": [
        "# Group dataframe by candidate name and then display contb_receipt_amt column\n",
        "# and count the total number of contribution\n",
        "donor_df.groupby('cand_nm')['contb_receipt_amt'].count()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cand_nm\n",
              "Bachmann, Michelle                 13082\n",
              "Cain, Herman                       20052\n",
              "Gingrich, Newt                     46883\n",
              "Huntsman, Jon                       4066\n",
              "Johnson, Gary Earl                  1234\n",
              "McCotter, Thaddeus G                  73\n",
              "Obama, Barack                     589127\n",
              "Paul, Ron                         143161\n",
              "Pawlenty, Timothy                   3844\n",
              "Perry, Rick                        12709\n",
              "Roemer, Charles E. 'Buddy' III      5844\n",
              "Romney, Mitt                      105155\n",
              "Santorum, Rick                     46245\n",
              "Name: contb_receipt_amt, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "id": "diEQb81DEncr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Obama is the front-runner in number of people donating, since he is not competeing with any other democratic nominees. Let's take a look at the total dollar amounts."
      ]
    },
    {
      "metadata": {
        "id": "afOsS-ZxErox",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "outputId": "9d6b36cd-aeb3-4ccc-9ee6-6cabb98b54f9"
      },
      "cell_type": "code",
      "source": [
        "# sum of all the contribution amounts\n",
        "cand_amount = donor_df.groupby('cand_nm')['contb_receipt_amt'].sum()\n",
        "\n",
        "# index tracker\n",
        "i = 0\n",
        "\n",
        "for don in cand_amount:\n",
        "    print(\"The candidate %s raised %.0f dollars \" %(cand_amount.index[i], don))\n",
        "    i += 1\n",
        "    \n",
        "cand_amount.plot(kind='bar', figsize=(12,6))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The candidate Bachmann, Michelle raised 2711439 dollars \n",
            "The candidate Cain, Herman raised 7101082 dollars \n",
            "The candidate Gingrich, Newt raised 12832770 dollars \n",
            "The candidate Huntsman, Jon raised 3330373 dollars \n",
            "The candidate Johnson, Gary Earl raised 566962 dollars \n",
            "The candidate McCotter, Thaddeus G raised 39030 dollars \n",
            "The candidate Obama, Barack raised 135877427 dollars \n",
            "The candidate Paul, Ron raised 21009620 dollars \n",
            "The candidate Pawlenty, Timothy raised 6004819 dollars \n",
            "The candidate Perry, Rick raised 20305754 dollars \n",
            "The candidate Roemer, Charles E. 'Buddy' III raised 373010 dollars \n",
            "The candidate Romney, Mitt raised 88335908 dollars \n",
            "The candidate Santorum, Rick raised 11043159 dollars \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f26fcd11a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S9MADD+jChQuKj4+XJNntdkVFRally5aSpFGjRql8+fK5GBcAAADIvkynQ8TGxsrX19dx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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "I_SCVPqaFGhA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "ab70b6eb-476e-45a8-e2e8-1786ce165064"
      },
      "cell_type": "code",
      "source": [
        "donor_df.groupby('Party')['contb_receipt_amt'].sum()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Party\n",
              "Democrat      1.358774e+08\n",
              "Republican    1.736539e+08\n",
              "Name: contb_receipt_amt, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "metadata": {
        "id": "YXutaCzuFIoj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "outputId": "d04a8bae-2c75-44cb-ca25-e4b4cf8cf958"
      },
      "cell_type": "code",
      "source": [
        "donor_df.groupby('Party')['contb_receipt_amt'].sum().plot(kind='bar')"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f26fcd111d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dPXpUZ86cUbdu3Tr8XklJSZc1/DXl1TqrJ4Cfgvp1GQqqN1o9AfwU7O+9jn758LlbOzk5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\nADAMcQYAwDDEGQAAw/w/th75DZ1xaAgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hg0ligMsFTcV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Looks like Obama couldn't compete against all the republicans, but he certainly has the advantage of their funding being splintered across multiple candidates.\n",
        "\n",
        "Finally to start closing out the project, let's look at donations and who they came from (as far as occupation is concerned). We will start by grabing the occupation information from the dono_df DataFrame and then using pivot_table to make the index defined by the various occupations and then have the columns defined by the Party (Republican or Democrat). FInally we'll also pass an aggregation function in the pivot table, in this case a simple sum function will add up all the contributions by anyone with the same profession."
      ]
    },
    {
      "metadata": {
        "id": "sup6RlKWFJJl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "a1ab91c8-2eec-49f2-9fcf-89e95bf72851"
      },
      "cell_type": "code",
      "source": [
        "# use a pivot table to extract and organize the data by the donor occupation\n",
        "occupation_df = donor_df.pivot_table('contb_receipt_amt',\n",
        "                                      index='contbr_occupation',\n",
        "                                      columns='Party', aggfunc='sum')\n",
        "occupation_df.head(5)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Party</th>\n",
              "      <th>Democrat</th>\n",
              "      <th>Republican</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>contbr_occupation</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MIXED-MEDIA ARTIST / STORYTELLER</th>\n",
              "      <td>100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AREA VICE PRESIDENT</th>\n",
              "      <td>250.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RESEARCH ASSOCIATE</th>\n",
              "      <td>100.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TEACHER</th>\n",
              "      <td>500.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>THERAPIST</th>\n",
              "      <td>3900.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Party                                Democrat  Republican\n",
              "contbr_occupation                                        \n",
              "   MIXED-MEDIA ARTIST / STORYTELLER     100.0         NaN\n",
              " AREA VICE PRESIDENT                    250.0         NaN\n",
              " RESEARCH ASSOCIATE                     100.0         NaN\n",
              " TEACHER                                500.0         NaN\n",
              " THERAPIST                             3900.0         NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "metadata": {
        "id": "RXztMy5ZFY9o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a13d54fa-5e43-4d8a-aac8-ffb17b7fc49c"
      },
      "cell_type": "code",
      "source": [
        "occupation_df.shape"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45067, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "dzaq0-C4FgR5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "There is over 45000 reported different occupations between all the donors. This is probably far too large to display effectively with a small, static visualization. What we should do is have a cut-off for total contribution amounts. Afterall, small donations of 20 dollars by one type of occupation won't give us too much insight. So let's set our cut off at 1 million dollars."
      ]
    },
    {
      "metadata": {
        "id": "VruSa8etFZcj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "00e3e53c-d076-40c9-f620-3eff62312ec8"
      },
      "cell_type": "code",
      "source": [
        "occupation_df.tail(5)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Party</th>\n",
              "      <th>Democrat</th>\n",
              "      <th>Republican</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>contbr_occupation</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ZOOKEEPER</th>\n",
              "      <td>35.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZOOLOGIST</th>\n",
              "      <td>400.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZOOLOGY EDUCATION</th>\n",
              "      <td>25.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>\\NONE\\</th>\n",
              "      <td>NaN</td>\n",
              "      <td>250.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>~</th>\n",
              "      <td>NaN</td>\n",
              "      <td>75.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Party              Democrat  Republican\n",
              "contbr_occupation                      \n",
              "ZOOKEEPER              35.0         NaN\n",
              "ZOOLOGIST             400.0         NaN\n",
              "ZOOLOGY EDUCATION      25.0         NaN\n",
              "\\NONE\\                  NaN       250.0\n",
              "~                       NaN        75.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "metadata": {
        "id": "HJWE8CZDFmOj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Since after all, a 35$ contribution by the zoo keeper isn't gonna have a big effect in the large scope of the campaign. So, we are gonna set a cut-off point at 1 milllion dollars of sum contributions."
      ]
    },
    {
      "metadata": {
        "id": "U-N9unbOFnh3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f643ff1f-f41b-4dc0-ee5e-dc01518ce920"
      },
      "cell_type": "code",
      "source": [
        "occupation_df = occupation_df[occupation_df.sum(1) > 1000000]\n",
        "occupation_df.shape"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(31, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "b7z98Z3YFn_t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "outputId": "6acd74b5-f1d9-4dbd-aacc-d9098412b3cb"
      },
      "cell_type": "code",
      "source": [
        "# cmap -> colormap\n",
        "# barh -> horizontal plot\n",
        "occupation_df.plot(kind='barh', figsize=(7,12), cmap='copper')"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f26fcc06550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NZGSk7V6/fv0wmUx88803NG/enEceeYQlS5Zw9OhRPvroI4YNG3ZZY/by8iIx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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 504x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "xEpZzexrF6mM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Looks like there are some occupations that are either mislabeled or aren't really occupations. Let's get rid of: Information Requested occupations and let's combine CEO and C.E.O."
      ]
    },
    {
      "metadata": {
        "id": "_xwcnLMWF7qm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "outputId": "4cfc1a65-80b3-4a32-d922-045fe87af30c"
      },
      "cell_type": "code",
      "source": [
        "# drop the unavailble occupations\n",
        "occupation_df.drop(['INFORMATION REQUESTED PER BEST EFFORTS','INFORMATION REQUESTED'], axis=0, inplace=True)\n",
        "\n",
        "# sets new ceo row as sum of the current two\n",
        "occupation_df.loc['CEO'] = occupation_df.loc['CEO'] + occupation_df.loc['C.E.O.']\n",
        "occupation_df.drop('C.E.O.', inplace=True)\n",
        "\n",
        "occupation_df.plot(kind='barh', figsize=(7,12), cmap='copper')"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f26fcabd400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FpQSKkWMUp5IpRo4pyzgVlZSVW1LQrVs33n//fR555BGaNGlCrVq17F6vUaMGFy9e5MSJ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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 504x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "bW3dI-exGMIB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Looks like CEOs are a little more conservative leaning, this may be due to the tax philosophies of each party during the election."
      ]
    }
  ]
}